The task of semantic segmentation, and by extension classification is a very challeging research, especially for point clouds. Specifically, deep learning techniques are interesting for their potential high level of performance and fully-fledge applicability (learning features that work best as part of the system). However, they are very constrained by the availability of high quality training datasets, even more challenging due to the various domain specialization thus labels.

Therefore, it is interesting to know how well unsupervised - in the sense that their is no need of training data - method compare to the best-performing state of the art 3D deep learning approaches ? The recent open access article provided holds some answers...

We are scanning so much, I know our data would be valuable to these researchers. At the end of this year we will have over 30,000 floors (entire FLOORS), of point clouds and 3D revit models. Seems this is the training dataset they need.

That indeed would be very valuable !! Especially if one wants to directly have a mapping between point cloud and BIM elements !
We could set up a POC over your data to see how good it can perform once you have these high quality datasets.

Especially, what is important is your "expertise" and interpretation of the point cloud in correct BIM elements, to insure the data quality translate into the learning model quality !